Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to Intervene Novel Coronavirus COVID-19 Outbreak

Abstract

Dating back from late December 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia, now known as lung inflammation caused by novel coronavirus (COVID-19). Cases have spread to other cities in China and more than 180 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency is perhaps one of the top concerns in the year of 2020 for governments all over the world. Till today, the coronavirus outbreak is still raging and has no sign of being under control in many countries. In this paper, we aim at drawing lessons from the COVID-19 outbreak process in China and using the experiences to help the interventions against the coronavirus wherever in need. To this end, we have built a system predicting hazard areas on the basis of confirmed infection cases with location information. The purpose is to warn people to avoid of such hot zones and reduce risks of disease transmission through droplets or contacts. We analyze the data from the daily official information release which are publicly accessible. Based on standard classification frameworks with reinforcements incrementally learned day after day, we manage to conduct thorough feature engineering from empirical studies, including geographical, demographic, temporal, statistical, and epidemiological features. Compared with heuristics baselines, our method has achieved promising overall performance in terms of precision, recall, accuracy, F1 score, and AUC. We expect that our efforts could be of help in the battle against the virus, the common opponent of human kind.